similar to: cdplot error

I am having difficulties interpreting a cdplot, I have a binary variable y that I want to cdplot against a continuous variable x,below is the R command cdplot(y~x, data=mydata) you get a plot with a dark shaded area and a light shaded area. what do these areas mean? and how to interpret the plot? you help is greatly appreciated -- View this message in context:

Hi, Given that cdplot() is used to produce the conditional density of a categorical y along a numerical x, it seems natural that it could be used with a date or time x (such as 'POSIXct'). Is this desirable? If so, I've created a patch that would allow this, by coercing the POSIXct x variable to produce the density, but use the original POSIXct x to draw the x axis. Index:

Hi, I have a dataset that I am trying to analyze and plot as an ordered logistic regression (y = ordinal categories 1-3, x = continuous variable with values 3-9). First is a problem with cdplot: Produces a beautiful plot, with the "right" trend, but my independent factor values are transformed. The factor has values from 3-9, but the plot produces an x-axis with values from 20-140.

Hello R Folks, I am new to R. I have been struggling to overlay a barchart with a xyplot together on one plot but did not get this worked out. Any help and idea are greatly appreciated. I attached R scripts for barchart and xyplot below and also data I used. What I am trying to do is just to put the barchart and xyplot together on one plot. Huapeng <<dispersal_infestation.csv>>

Hi I'm encountering some problems with coxme My data: I'm looking at the survival of animals in an experiment with 3 treatments, which came from 4 different populations, two of which were infected with a parasite and two of which were not. I'm interested if infected animals differe from uninfected ones across treatments. Factor 1: treatment (3 levels) Factor 2: infection state

Hi, I need help creating a code for a multiple BACI design (Before-After Control-Impact) ANOVA. I'm new to R and basically need to run a complex mixed model ANOVA that treats location as a random factor. Data are from a fire experiment, run 2001-2004 (2 years pre, 2 years post). Response is bird abundance. 4 Treatments had 3 replicates each (forest stands): 1. Control, 2. Prescribed fire

Dear R community, I'm trying to analyze a model with an ordinal response variable. I wonder if clm()s (Cumulative Link Models) are appropriate in my case. The study compares parasite infestation of porpoises in 1995 and 2009. The degree of infestation is a rank (mild to severe, as ordered factor). In some parasite species clm gives meaningful results. But in one case I started to wonder. In

This may be a newbie question - although I did search for this error message in the archives and via google and didn't see this error: The help page for "scan" indicates that among the types of data capable of being read are: > "The supported types are 'logical', 'integer', 'numeric', 'complex', 'character', 'raw' and

On Thu, 19 Jul 2018 08:11:40 +0200 Steffen Kaiser <skdovecot at inf.h-brs.de> wrote: > > On Thu, 19 Jul 2018, Mark Foley wrote: > > On Wed, 18 Jul 2018 07:23:06 +0200 Steffen Kaiser <skdovecot at inf.h-brs.de> wrote: > >> > >> On Tue, 17 Jul 2018, Mark Foley wrote: > >>> On Tue, 17 Jul 2018 08:06:24 +0200 Steffen Kaiser <skdovecot at

Hi all, Firstly excuse-me by this off-topic question. I'm very confused to decide what is the better analysis to use. Whem the experiments is a tradicional design of manipulative experiments, it is easy to decide. But in natural observations is too complicated. I have 6 farms, each farm cultives some sugarcane's varieties (maybe or not the same varieties by farm). For each farm I make

Dear all, I am quite new with R and I have a problem with plotting a binomial regression line in a plot. This is what I type in: > model<-glm(Para~Size,binomial) > par(mfrow=c(1,1)) > xv<-seq(3.2,4.5,0.01) > yv<-predict(model,list(area=xv),type="response") > plot(Size,Para) > lines(xv,yv) The error message that I get is: > Error in xy.coords(x, y) :

I have two series of events over time and I want to construct a graph of the relative frequency/density of these events that allows their distributions to be sensibly compared. The events are the milestones items in my project on milestones in the history of data visualization [1], and I want to compare trends in Europe vs. North America. I decided to use a graph of two overlaid density

Hi Olivier, Thanks for responding! I get to learn this way. On Feb 17, 2010, at 12:50, Olivier Meurant wrote: > Hi Garrison, > > I am not a specialist of the code but here is my 2 cents: > > - I like the idea that in lazy-mode the call (in module or not) is treated by a stub (like every calls). If we go further with this, I'll have to add test cases for lazy mode. I kind

Hello, suppose I have a multivariate multiple regression model such as the following: > DF<-data.frame(x1=rep(c(0,1),each=50),x2=rep(c(0,1),50)) > tmp<-rnorm(100) > DF$y1<-tmp+DF$x1*.5+DF$x2*.3+rnorm(100,0,.5) > DF$y2<-tmp+DF$x1*.5+DF$x2*.7+rnorm(100,0,.5) > x.mlm<-lm(cbind(y1,y2)~x1+x2,data=DF) > coef(x.mlm) y1 y2 (Intercept)

On Wed, 18 Jul 2018 07:23:06 +0200 Steffen Kaiser <skdovecot at inf.h-brs.de> wrote: > > On Tue, 17 Jul 2018, Mark Foley wrote: > > On Tue, 17 Jul 2018 08:06:24 +0200 Steffen Kaiser <skdovecot at inf.h-brs.de> wrote: > >> > >> On Mon, 16 Jul 2018, Mark Foley wrote: > >> > >>> We had a user quit recently. Three days ago I copied his

Hello all ;) Tell I have a Xeon server with 4 vcpus If on this box I have some guets, tell 10, what is the best solutions : - Set all guests to 1 vcpu ? - Set all guests to 4 vcpus ? I well understand that if I set a guest with 4 vcpus and all others only with one vpcus, then the one with 4 vcpus will have more "cpu time" available than others guests. But in case I''d like

Hi there, For years I've been using the ext3 file system without to think that it can ever gets broken so bad. This was until last week when a box that I have running Linux from a SanDisk CF went down. Since then I am struggling with this CF trying to understand what is happening. The CF is SanDisk ultra II 1GB. On this I have 4 partitions all of them with ext3: boot / swap

Good day, I wasn't sure whether this should go to the user list or the samba-technical list. I chose here based on the descriptions of the list. Forgive me if my understanding of the naming is inaccurate. It is my understanding that Samba3 (and I believe 4, as well) has a very powerful SID<->UID mapping mechanism which will auto create the UID in a range. This is what I mean by Unix ID

In thinking about this we could use a Mutex::tryacquire(...) (non-recursive), around JIT::runJITOnFunctionUnlocked(...)'s while loop, and use your JITEmitter:: getLazyFunctionStub(...) suggestion in place of forceEmitFunctionStub(...). Is the lock attempt too heavy, even if it is implemented with atomics? I'll implement this when I have time. Garrison On Feb 17, 2010, at 15:42, Garrison

Dr. Stevens, Hi, my name is Trey Scott, and I'm a grad student of Brian McCarthy's. He referred me to you because of your expertise in handling complex R problems. We were hoping you could help us solve a nagging problem that is prohibiting me from producing graphicl output. Here is a simple mock-up of the matrix I'm using a b c d e f 1i 1 4